A Hybrid Planning System for Smart Charging of Electric Fleets

Abstract

Electric vehicle (EV) fleets are well suited for last-mile deliveries both from sustainability and operational cost perspectives. To ensure economic parity with non-EV options, even captive chargers for EV fleets need to be managed intelligently. Specifically, the EVs needs to be adequately charged for their entire delivery runs while handling reduced time flexibility between runs; limited number of chargers; and deviations from the planned schedule. Existing works either solve smaller instances of this problem optimally, or larger instances with significant sub-optimality. In addition, they typically consider either day-ahead or real-time planning in isolation. We complement existing works with a hybrid approach that first identifies a day-ahead plan for assigning EVs to chargers; and then uses online replanning to handle any deviations in real-time. For the day-ahead planning, we use a learning agent (LA) that learns to assign EVs to chargers over several problem instances. Because the agent solves a given instance during its testing phase, it achieves scale in problem size with limited sub-optimality. For the online replanning, we use a greedy heuristic that dynamically refines the day-ahead plan to handle delays in EV arrivals. We evaluate our approach using representative datasets. As baselines for the LA, we use an exact mixed-integer linear program (MILP) (greedy heuristic) for small (large) problem instances. As baselines for the replanning, we use no-planning and no-replanning. Our experiments show that LA performs better (8.5-14%) than greedy heuristic in large problem instances, while being reasonably close (< 22%) to the optimal in smaller instances. For online replanning, our approach performs about 7-20% better than no-planning and no-replanning for a range of delay profiles.